pcvcrossval {pcv}

Generate sequence of indices for cross-validation

Description

Generates and returns sequence of object indices for each segment in random segmented cross-validation

Usage

pcvcrossval(cv = 1, nobj = NULL, resp = NULL)

Arguments

cv

cross-validation settings, can be a number, a list or a vector with integers.

nobj

number of objects in a dataset

resp

vector or matrix with response values to use in case of venetian blinds

Details

Parameter 'cv' defines how to split the rows of the training set. The split is similar to cross-validation splits, as PCV is based on cross-validation. This parameter can have the following values:

1. A number (e.g. 'cv = 4'). In this case this number specifies number of segments for random splits, except 'cv = 1' which is a special case for leave-one-out (full cross-validation).

2. A list with 2 values: 'list("name", nseg)'. In this case '"name"' defines the way to make the split, you can select one of the following: '"loo"' for leave-one-out, '"rand"' for random splits or '"ven"' for Venetian blinds (systematic) splits. The second parameter, 'nseg', is a number of segments for splitting the rows into. For example, 'cv = list("ven", 4)', shown in the code examples above, tells PCV to use Venetian blinds splits with 4 segments.

3. A vector with integer numbers, e.g. 'cv = c(1, 2, 3, 1, 2, 3, 1, 2, 3)'. In this case number of values in this vector must be the same as number of rows in the training set. The values specify which segment a particular row will belong to. In case of the example shown here, it is assumed that you have 9 rows in the calibration set, which will be split into 3 segments. The first segment will consist of measurements from rows 1, 4 and 7.

Value

vector with object indices for each segment